06:27PM EDT - A: 1st gen does not do much for sparse-ness. Future products not disclosed in this. Reduced precision is fundamental. We'd love to know where the limit for training and inference is in lower precision

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Waaaaay over my head, I couldn't understand most of what was written. How exactly does Google use these for neural networks? What aspects of Google Search lend themselves to neural network processing? And how would these cards compare to neuron-like chips being developed by IBM? Reply

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Google does a lot more than perform searches. Google uses neural networks for image recognition, translation services, speech recognition, recommendations, ad targeting, self-driving cars, YouTube censorship, beating the world's top go players, and probably other things. As far as search, they are possibly used in interpreting inputted search strings and judging the relevance of search results.

IBM's TrueNorth chip, part of a class called neuromorphic computers, is very different from a TPU. Traditional computers have processing units together in one place (such as a CPU), the memory together in another (a RAM chip), and the communication function together. The TPU works this way, even if they are optimizing the data flow by trying to store relevant data close to the processing units. Neuromorphic devices are instead broken up into units with each unit having processing, memory, and communications functionality. The units, which are modeled after neurons, are formed together in a network so that one of the inputs of a unit might be the output of several others. When an input goes into a unit, the neuron "spikes" and sends an output based on the inputs and I believe whatever internal state the unit had. The neurons are event driven instead of running on a clock like a traditional processor.

So, put another way, neural networks are abstract structures modeled after the way the brain works. TPUs are processors designed to take these neural networks and perform manipulations on them, but they do so by using traditional computing architecture, and not by following the model of our brain. Neuromorphic devices like IBM's True North are trying to operate closer to how the neurons in the brain actually operate (they generally don't try to replicate the way the brain works, they just take inspiration from key facets of its operation). Obviously, in theory, neuromorphic devices should be very good candidates to use to operate neural networks, providing you can make an effective one and you can program it effectively.

The advantage of a neuromorphic device is that it avoids the bottleneck associated with moving data between storage and processing units, which uses a whole lot of power. However, programming for them is very different and so I think we don't really know how to get them to do what we want, yet.Reply

Dont sell yourself short, that explanation really helped with understanding what TPU is about, and if i can find the time I am encouraged to go and look for more information about this. Its why my first read of the day is still Anandtech after all these years, partly for the articles and partly for the comments, trolls, gnomes and goblins included. Authors and commentors keep up the hard work :) Reply

Lots of comparison to GK210 rather than GP100/GV100. I understand Google want to say "those were 2015 era and we made the TPU in 2015", but things have changed quite a bit in the GPU realm since then. Reply

It certainly made sense when they did the tests in 2015. It was iffy when they released the paper publicly in 2016. But I find it disappointing that they are still seemingly presenting the data as if its currently relevant in 2017.

The K80 is a dinosaur in terms of machine learning. The problem is they compare their "TPU" to "GPU" and "CPU" in their 2017 presentation. "GPU" has continued on and changed massively for machine learning workloads since their tests, but their labeling gives an impression that their presented results somehow extrapolate to the GPU lineage and so are still relevant. By now they should not be trying to make such generalizations from their results with 3 generations old hardware.

In my opinion, either they should cast their talk as a historic background of the development and introduction of their TPU, and probably change their charts to say K80 instead of "GPU", or if they are going to give the talk as if its currently relevant they should update their tests with current comparisons.Reply

They had a 10x diff to CPU and GPU, that won't have been wiped out by the 3%/year improvements in CPU or 50%/year in GPU... I don't disagree a more current comparison would have been nice but this is far from irrelevant. Reply